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LLMs and generative AI / Fine tuning and alignment

Efficient LLM Fine-Tuning with LoRA

16 min read, 14 sections

Contents
  1. LoRA Implementation Fundamentals
  2. QLoRA Implementation with 4-bit Quantization
  3. Dynamic Rank Adjustment in DyLoRA
  4. LoRA-Drop Implementation
  5. DoRA Implementation with Weight Decomposition
  6. Efficient Parameter Merging
  7. Training Loop Implementation
  8. Memory-Efficient Backpropagation
  9. Adaptive Rank Selection for DyLoRA
  10. LoRA Cross-Attention Implementation
  11. LoRA with Gradient Checkpointing
  12. Results Analysis and Visualization
  13. Hybrid LoRA with Pruning
  14. Additional Resources

LoRA Implementation Fundamentals

Low-Rank Adaptation fundamentally works by decomposing weight updates into smaller matrices through SVD-like decomposition, enabling efficient fine-tuning while maintaining model quality. The implementation starts with basic matrix operations to demonstrate the core concept.

import numpy as np

class LoRALayer:
    def __init__(self, in_features, out_features, rank=4):
        self.rank = rank
        # Initialize matrices A and B for low-rank decomposition
        self.A = np.random.randn(in_features, rank) / np.sqrt(rank)
        self.B = np.random.randn(rank, out_features) / np.sqrt(rank)
        
    def forward(self, x):
        # Compute low-rank update
        return x @ (self.A @ self.B)

# Example usage
layer = LoRALayer(768, 768, rank=8)
input_tensor = np.random.randn(32, 768)
output = layer.forward(input_tensor)
print(f"Input shape: {input_tensor.shape}")
print(f"Output shape: {output.shape}")
print(f"Parameter reduction: {(768*8 + 8*768)/(768*768)*100:.2f}%")

QLoRA Implementation with 4-bit Quantization

Quantized LoRA reduces memory usage through 4-bit quantization while maintaining model performance. This implementation demonstrates the basic quantization process and dequantization during forward pass.

import numpy as np

class QuantizedLoRALayer:
    def __init__(self, in_features, out_features, rank=4, bits=4):
        self.rank = rank
        self.bits = bits
        self.scale = (2**(bits-1)) - 1
        
        # Initialize and quantize matrices
        self.A = self._quantize(np.random.randn(in_features, rank) / np.sqrt(rank))
        self.B = self._quantize(np.random.randn(rank, out_features) / np.sqrt(rank))
        
    def _quantize(self, x):
        # Quantize to n-bit integers
        return np.round(np.clip(x * self.scale, -self.scale, self.scale))
    
    def _dequantize(self, x):
        # Convert back to floating point
        return x / self.scale
    
    def forward(self, x):
        # Dequantize during forward pass
        A_dequant = self._dequantize(self.A)
        B_dequant = self._dequantize(self.B)
        return x @ (A_dequant @ B_dequant)

# Example usage
layer = QuantizedLoRALayer(768, 768, rank=8, bits=4)
input_tensor = np.random.randn(32, 768)
output = layer.forward(input_tensor)
print(f"Memory savings: {(32/4)*100:.2f}%")

Dynamic Rank Adjustment in DyLoRA

DyLoRA introduces adaptive rank selection during training, optimizing the trade-off between model capacity and computational efficiency. This implementation shows the dynamic rank adjustment mechanism based on loss gradients.

import numpy as np
from typing import List

class DyLoRALayer:
    def __init__(self, in_features, out_features, ranks: List[int] = [4, 8, 16]):
        self.ranks = ranks
        self.current_rank_idx = 0
        self.matrices = {
            rank: (np.random.randn(in_features, rank) / np.sqrt(rank),
                  np.random.randn(rank, out_features) / np.sqrt(rank))
            for rank in ranks
        }
    
    def adjust_rank(self, loss_gradient):
        # Simulate rank adjustment based on loss gradient
        gradient_norm = np.linalg.norm(loss_gradient)
        if gradient_norm > 1.0 and self.current_rank_idx < len(self.ranks) - 1:
            self.current_rank_idx += 1
        elif gradient_norm < 0.1 and self.current_rank_idx > 0:
            self.current_rank_idx -= 1
            
    def forward(self, x):
        current_rank = self.ranks[self.current_rank_idx]
        A, B = self.matrices[current_rank]
        return x @ (A @ B)

# Example usage
layer = DyLoRALayer(768, 768)
input_tensor = np.random.randn(32, 768)
fake_gradient = np.random.randn(32, 768)

for _ in range(3):
    output = layer.forward(input_tensor)
    layer.adjust_rank(fake_gradient)
    print(f"Current rank: {layer.ranks[layer.current_rank_idx]}")

LoRA-Drop Implementation

LoRA-Drop enhances training efficiency by selectively dropping certain adaptation matrices during training, similar to dropout but specifically designed for low-rank adaptations. This implementation demonstrates the core dropping mechanism.

import numpy as np

class LoRADropLayer:
    def __init__(self, in_features, out_features, rank=4, drop_prob=0.3):
        self.rank = rank
        self.drop_prob = drop_prob
        self.A = np.random.randn(in_features, rank) / np.sqrt(rank)
        self.B = np.random.randn(rank, out_features) / np.sqrt(rank)
        self.drop_mask = None
        
    def create_drop_mask(self, batch_size):
        # Create binary mask for dropping
        return np.random.binomial(1, 1-self.drop_prob, 
                                size=(batch_size, self.rank))
        
    def forward(self, x, training=True):
        if training:
            self.drop_mask = self.create_drop_mask(x.shape[0])
            # Apply mask and scale
            dropped_output = x @ (self.A @ self.B)
            return dropped_output * self.drop_mask[:, :, np.newaxis]
        return x @ (self.A @ self.B)

# Example usage
layer = LoRADropLayer(768, 768, rank=8)
input_tensor = np.random.randn(32, 768)

# Training mode
train_output = layer.forward(input_tensor, training=True)
print(f"Training output shape: {train_output.shape}")
print(f"Active adaptations: {np.mean(layer.drop_mask) * 100:.2f}%")

# Inference mode
eval_output = layer.forward(input_tensor, training=False)
print(f"Inference output shape: {eval_output.shape}")

DoRA Implementation with Weight Decomposition

DoRA separates pretrained weights into magnitude and direction components, allowing for more stable updates during fine-tuning while maintaining the original model's knowledge. The implementation shows this decomposition process.

import numpy as np

class DoRALayer:
    def __init__(self, in_features, out_features, rank=4):
        # Initialize pretrained weights
        self.pretrained_weights = np.random.randn(in_features, out_features)
        
        # Decompose into magnitude and direction
        self.magnitude = np.linalg.norm(self.pretrained_weights, axis=1, keepdims=True)
        self.direction = self.pretrained_weights / (self.magnitude + 1e-6)
        
        # LoRA matrices for updates
        self.A = np.random.randn(in_features, rank) / np.sqrt(rank)
        self.B = np.random.randn(rank, out_features) / np.sqrt(rank)
        
    def forward(self, x, alpha=0.1):
        # Combine original decomposed weights with LoRA updates
        lora_update = self.A @ self.B
        updated_direction = self.direction + alpha * lora_update
        updated_direction = updated_direction / (np.linalg.norm(updated_direction, 
                                              axis=1, keepdims=True) + 1e-6)
        return x @ (self.magnitude * updated_direction)

# Example usage
layer = DoRALayer(512, 512, rank=8)
input_tensor = np.random.randn(16, 512)
output = layer.forward(input_tensor)
print(f"Output shape: {output.shape}")
print(f"Direction matrix norm: {np.mean(np.linalg.norm(layer.direction, axis=1)):.4f}")

Efficient Parameter Merging

Implementation of efficient parameter merging for LoRA adaptation, enabling seamless integration of multiple fine-tuned adaptations into a single model while maintaining performance characteristics.

import numpy as np
from typing import List, Tuple

class LoRAMerger:
    def __init__(self, base_model_dim: int, rank: int):
        self.base_dim = base_model_dim
        self.rank = rank
        self.adaptations: List[Tuple[np.ndarray, np.ndarray]] = []
        
    def add_adaptation(self, A: np.ndarray, B: np.ndarray, weight: float = 1.0):
        """Add a new LoRA adaptation with specified weight."""
        self.adaptations.append((A * weight, B))
        
    def merge(self) -> np.ndarray:
        """Merge all adaptations into a single weight update matrix."""
        merged_weights = np.zeros((self.base_dim, self.base_dim))
        
        for A, B in self.adaptations:
            merged_weights += A @ B
            
        return merged_weights

# Example usage
merger = LoRAMerger(base_model_dim=768, rank=8)

# Add multiple adaptations
for i in range(3):
    A = np.random.randn(768, 8) / np.sqrt(8)
    B = np.random.randn(8, 768) / np.sqrt(8)
    merger.add_adaptation(A, B, weight=0.3)

final_weights = merger.merge()
print(f"Merged weights shape: {final_weights.shape}")
print(f"Parameter compression ratio: {(768*8*2*3)/(768*768)*100:.2f}%")

Training Loop Implementation

A complete training loop implementation for LoRA fine-tuning, demonstrating gradient computation, weight updates, and adaptation scheduling during the training process.

import numpy as np
from typing import Optional

class LoRATrainer:
    def __init__(self, model_dim: int, rank: int, learning_rate: float = 0.001):
        self.model_dim = model_dim
        self.rank = rank
        self.lr = learning_rate
        
        # Initialize LoRA matrices
        self.A = np.random.randn(model_dim, rank) / np.sqrt(rank)
        self.B = np.random.randn(rank, model_dim) / np.sqrt(rank)
        
        # Gradient accumulators
        self.A_grad = np.zeros_like(self.A)
        self.B_grad = np.zeros_like(self.B)
        
    def compute_gradients(self, input_data: np.ndarray, 
                         target: np.ndarray, 
                         base_output: Optional[np.ndarray] = None):
        # Simplified gradient computation
        lora_output = input_data @ (self.A @ self.B)
        if base_output is not None:
            total_output = base_output + lora_output
        else:
            total_output = lora_output
            
        # Compute error
        error = total_output - target
        
        # Compute gradients
        self.A_grad = input_data.T @ (error @ self.B.T)
        self.B_grad = self.A.T @ (input_data.T @ error)
        
        return np.mean(error ** 2)  # Return MSE loss
        
    def update_weights(self):
        # Apply gradients
        self.A -= self.lr * self.A_grad
        self.B -= self.lr * self.B_grad
        
        # Reset gradients
        self.A_grad.fill(0)
        self.B_grad.fill(0)

# Example training loop
trainer = LoRATrainer(model_dim=512, rank=8, learning_rate=0.001)
batch_size = 32

# Simulate training data
input_data = np.random.randn(batch_size, 512)
target_data = np.random.randn(batch_size, 512)

# Training loop
for epoch in range(5):
    loss = trainer.compute_gradients(input_data, target_data)
    trainer.update_weights()
    print(f"Epoch {epoch+1}, Loss: {loss:.4f}")

Memory-Efficient Backpropagation

Implementation of memory-efficient backpropagation for LoRA, utilizing gradient checkpointing and selective computation to reduce memory requirements during training.

import numpy as np
from typing import List, Optional

class MemoryEfficientLoRA:
    def __init__(self, 
                 layer_dims: List[int], 
                 rank: int,
                 checkpoint_freq: int = 2):
        self.dims = layer_dims
        self.rank = rank
        self.checkpoint_freq = checkpoint_freq
        
        # Initialize layers
        self.layers = []
        for i in range(len(layer_dims) - 1):
            self.layers.append({
                'A': np.random.randn(layer_dims[i], rank) / np.sqrt(rank),
                'B': np.random.randn(rank, layer_dims[i+1]) / np.sqrt(rank)
            })
            
        self.checkpoints: List[Optional[np.ndarray]] = []
        
    def forward(self, x: np.ndarray, store_checkpoints: bool = True) -> np.ndarray:
        self.checkpoints = []
        current = x
        
        for i, layer in enumerate(self.layers):
            if store_checkpoints and i % self.checkpoint_freq == 0:
                self.checkpoints.append(current.copy())
            else:
                self.checkpoints.append(None)
                
            current = current @ (layer['A'] @ layer['B'])
            
        return current
    
    def backward(self, grad_output: np.ndarray, learning_rate: float = 0.001):
        current_grad = grad_output
        
        for i in range(len(self.layers) - 1, -1, -1):
            layer = self.layers[i]
            
            # Recompute forward pass if needed
            if self.checkpoints[i] is None:
                current = self.checkpoints[max(0, i - self.checkpoint_freq)]
                for j in range(max(0, i - self.checkpoint_freq), i):
                    current = current @ (self.layers[j]['A'] @ self.layers[j]['B'])
            else:
                current = self.checkpoints[i]
                
            # Compute gradients
            A_grad = current.T @ (current_grad @ layer['B'].T)
            B_grad = layer['A'].T @ (current.T @ current_grad)
            
            # Update weights
            layer['A'] -= learning_rate * A_grad
            layer['B'] -= learning_rate * B_grad
            
            # Update gradient for next layer
            current_grad = current_grad @ layer['B'].T @ layer['A'].T

# Example usage
model = MemoryEfficientLoRA(layer_dims=[768, 512, 256], rank=8)
input_data = np.random.randn(32, 768)
output = model.forward(input_data)
grad_output = np.random.randn(*output.shape)
model.backward(grad_output)

print(f"Peak memory checkpoints: {len(model.checkpoints)}")
print(f"Memory savings: {(1 - len([c for c in model.checkpoints if c is not None])/len(model.checkpoints))*100:.1f}%")

Adaptive Rank Selection for DyLoRA

Implementation of an adaptive rank selection mechanism that automatically adjusts the LoRA rank based on task complexity and performance metrics during training, optimizing computational resources.

import numpy as np
from typing import List, Dict

class AdaptiveRankLoRA:
    def __init__(self, 
                 input_dim: int,
                 output_dim: int,
                 rank_options: List[int] = [4, 8, 16, 32],
                 eval_interval: int = 100):
        self.input_dim = input_dim
        self.output_dim = output_dim
        self.rank_options = rank_options
        self.eval_interval = eval_interval
        self.step_counter = 0
        self.performance_history: Dict[int, List[float]] = {r: [] for r in rank_options}
        
        # Initialize multiple ranks
        self.adaptations = {
            rank: self._init_matrices(rank) for rank in rank_options
        }
        self.current_rank = rank_options[0]
        
    def _init_matrices(self, rank: int) -> tuple:
        A = np.random.randn(self.input_dim, rank) / np.sqrt(rank)
        B = np.random.randn(rank, self.output_dim) / np.sqrt(rank)
        return (A, B)
    
    def evaluate_rank_performance(self, loss: float):
        self.performance_history[self.current_rank].append(loss)
        
        if self.step_counter % self.eval_interval == 0:
            # Calculate moving average of performance
            recent_performance = np.mean(self.performance_history[self.current_rank][-10:])
            
            # Adjust rank based on performance
            current_idx = self.rank_options.index(self.current_rank)
            if recent_performance > 0.1 and current_idx < len(self.rank_options) - 1:
                self.current_rank = self.rank_options[current_idx + 1]
            elif recent_performance < 0.05 and current_idx > 0:
                self.current_rank = self.rank_options[current_idx - 1]
    
    def forward(self, x: np.ndarray) -> np.ndarray:
        A, B = self.adaptations[self.current_rank]
        self.step_counter += 1
        return x @ (A @ B)

# Example usage
model = AdaptiveRankLoRA(input_dim=512, output_dim=512)
for step in range(300):
    input_data = np.random.randn(16, 512)
    output = model.forward(input_data)
    
    # Simulate loss computation
    fake_loss = 0.1 * np.exp(-step/100)
    model.evaluate_rank_performance(fake_loss)
    
    if step % 100 == 0:
        print(f"Step {step}, Current Rank: {model.current_rank}")
        print(f"Loss: {fake_loss:.4f}")

LoRA Cross-Attention Implementation

An implementation of LoRA for transformer cross-attention layers, demonstrating how to apply low-rank adaptation to attention mechanisms while maintaining computational efficiency.

import numpy as np

class LoRACrossAttention:
    def __init__(self, 
                 hidden_size: int,
                 num_heads: int,
                 lora_rank: int = 8,
                 lora_alpha: float = 16):
        self.hidden_size = hidden_size
        self.num_heads = num_heads
        self.head_dim = hidden_size // num_heads
        self.lora_rank = lora_rank
        self.scaling = lora_alpha / lora_rank
        
        # Initialize LoRA matrices for Q, K, V
        self.query_lora = self._init_lora_pair()
        self.key_lora = self._init_lora_pair()
        self.value_lora = self._init_lora_pair()
        
    def _init_lora_pair(self) -> tuple:
        A = np.random.randn(self.hidden_size, self.lora_rank) / np.sqrt(self.lora_rank)
        B = np.random.randn(self.lora_rank, self.hidden_size) / np.sqrt(self.lora_rank)
        return (A, B)
    
    def _apply_lora(self, x: np.ndarray, lora_pair: tuple) -> np.ndarray:
        A, B = lora_pair
        return x @ (A @ B) * self.scaling
    
    def forward(self, 
                query: np.ndarray,
                key: np.ndarray,
                value: np.ndarray,
                mask: np.ndarray = None) -> np.ndarray:
        batch_size = query.shape[0]
        
        # Apply LoRA to Q, K, V
        q = query + self._apply_lora(query, self.query_lora)
        k = key + self._apply_lora(key, self.key_lora)
        v = value + self._apply_lora(value, self.value_lora)
        
        # Reshape for multi-head attention
        q = q.reshape(batch_size, -1, self.num_heads, self.head_dim)
        k = k.reshape(batch_size, -1, self.num_heads, self.head_dim)
        v = v.reshape(batch_size, -1, self.num_heads, self.head_dim)
        
        # Compute attention scores
        scores = np.matmul(q, k.transpose(0, 1, 3, 2)) / np.sqrt(self.head_dim)
        
        if mask is not None:
            scores = scores + mask
        
        attention_weights = np.softmax(scores, axis=-1)
        
        # Apply attention to values
        output = np.matmul(attention_weights, v)
        output = output.reshape(batch_size, -1, self.hidden_size)
        
        return output

# Example usage
attention = LoRACrossAttention(hidden_size=512, num_heads=8)
batch_size, seq_length = 4, 32

# Generate sample inputs
query = np.random.randn(batch_size, seq_length, 512)
key = np.random.randn(batch_size, seq_length, 512)
value = np.random.randn(batch_size, seq_length, 512)

# Create attention mask (optional)
mask = np.triu(np.ones((seq_length, seq_length)) * -1e9, k=1)

output = attention.forward(query, key, value, mask)
print(f"Output shape: {output.shape}")
print(f"Memory efficiency: {(3*512*8 + 8*512)/(512*512*3)*100:.2f}% of original parameters")

LoRA with Gradient Checkpointing

Advanced implementation of LoRA utilizing gradient checkpointing for handling larger models while maintaining memory efficiency during training phases.

import numpy as np
from typing import Optional, List, Tuple

class CheckpointedLoRA:
    def __init__(self, 
                 layer_sizes: List[int],
                 lora_rank: int,
                 checkpoint_granularity: int = 2):
        self.layer_sizes = layer_sizes
        self.rank = lora_rank
        self.granularity = checkpoint_granularity
        
        # Initialize layers
        self.layers = []
        for i in range(len(layer_sizes)-1):
            self.layers.append({
                'A': np.random.randn(layer_sizes[i], lora_rank) / np.sqrt(lora_rank),
                'B': np.random.randn(lora_rank, layer_sizes[i+1]) / np.sqrt(lora_rank),
                'activation': np.zeros((0,))  # Placeholder for checkpointing
            })
            
    def _forward_segment(self, 
                        x: np.ndarray, 
                        start_idx: int, 
                        end_idx: int) -> np.ndarray:
        current = x
        for i in range(start_idx, end_idx):
            current = current @ (self.layers[i]['A'] @ self.layers[i]['B'])
            current = np.maximum(current, 0)  # ReLU activation
        return current
    
    def forward(self, x: np.ndarray, save_memory: bool = True) -> np.ndarray:
        if not save_memory:
            return self._forward_segment(x, 0, len(self.layers))
            
        # Save intermediate activations at checkpoints
        checkpoints: List[Tuple[int, np.ndarray]] = [(0, x)]
        current = x
        
        for i in range(0, len(self.layers), self.granularity):
            end_idx = min(i + self.granularity, len(self.layers))
            current = self._forward_segment(current, i, end_idx)
            if end_idx < len(self.layers):
                checkpoints.append((end_idx, current.copy()))
                
        return current, checkpoints
    
    def backward(self, 
                grad_output: np.ndarray,
                checkpoints: List[Tuple[int, np.ndarray]],
                learning_rate: float = 0.001) -> None:
        current_grad = grad_output
        
        for checkpoint_idx in range(len(checkpoints)-1, -1, -1):
            start_idx, activation = checkpoints[checkpoint_idx]
            end_idx = min(start_idx + self.granularity, len(self.layers))
            
            # Recompute forward pass for segment
            segment_input = activation
            intermediate_values = []
            current = segment_input
            
            for i in range(start_idx, end_idx):
                intermediate_values.append(current)
                current = current @ (self.layers[i]['A'] @ self.layers[i]['B'])
                current = np.maximum(current, 0)  # ReLU
                
            # Backward pass for segment
            for i in range(end_idx-1, start_idx-1, -1):
                # Gradient through ReLU
                current_grad = current_grad * (intermediate_values[i-start_idx] > 0)
                
                # Compute gradients for LoRA matrices
                A_grad = intermediate_values[i-start_idx].T @ (current_grad @ self.layers[i]['B'].T)
                B_grad = self.layers[i]['A'].T @ (intermediate_values[i-start_idx].T @ current_grad)
                
                # Update weights
                self.layers[i]['A'] -= learning_rate * A_grad
                self.layers[i]['B'] -= learning_rate * B_grad
                
                # Propagate gradient
                if i > start_idx:
                    current_grad = current_grad @ self.layers[i]['B'].T @ self.layers[i]['A'].T

# Example usage
model = CheckpointedLoRA(layer_sizes=[512, 256, 128, 64], lora_rank=8)
batch_size = 32
input_data = np.random.randn(batch_size, 512)

# Forward pass with checkpointing
output, checkpoints = model.forward(input_data)
grad_output = np.random.randn(*output.shape)

# Backward pass using checkpoints
model.backward(grad_output, checkpoints)

print(f"Memory saved: {(1 - len(checkpoints)/len(model.layers))*100:.1f}%")
print(f"Output shape: {output.shape}")

Results Analysis and Visualization

Implementation of comprehensive metrics and visualization tools for analyzing LoRA adaptation performance and memory efficiency across different configurations.

import numpy as np
import matplotlib.pyplot as plt
from typing import Dict, List

class LoRAAnalyzer:
    def __init__(self):
        self.metrics: Dict[str, List[float]] = {
            'memory_usage': [],
            'training_loss': [],
            'adaptation_efficiency': [],
            'rank_distribution': []
        }
        
    def compute_memory_usage(self, 
                           input_dim: int,
                           output_dim: int,
                           rank: int) -> float:
        full_params = input_dim * output_dim
        lora_params = rank * (input_dim + output_dim)
        return lora_params / full_params
    
    def track_metrics(self,
                     loss: float,
                     rank: int,
                     input_dim: int,
                     output_dim: int):
        memory = self.compute_memory_usage(input_dim, output_dim, rank)
        efficiency = 1.0 - loss * (memory ** 0.5)  # Simplified efficiency metric
        
        self.metrics['memory_usage'].append(memory)
        self.metrics['training_loss'].append(loss)
        self.metrics['adaptation_efficiency'].append(efficiency)
        self.metrics['rank_distribution'].append(rank)
    
    def plot_metrics(self):
        fig, axes = plt.subplots(2, 2, figsize=(15, 10))
        
        # Plot memory usage
        axes[0, 0].plot(self.metrics['memory_usage'])
        axes[0, 0].set_title('Memory Usage Over Time')
        axes[0, 0].set_ylabel('Memory Usage Ratio')
        
        # Plot training loss
        axes[0, 1].plot(self.metrics['training_loss'])
        axes[0, 1].set_title('Training Loss')
        axes[0, 1].set_ylabel('Loss')
        
        # Plot efficiency
        axes[1, 0].plot(self.metrics['adaptation_efficiency'])
        axes[1, 0].set_title('Adaptation Efficiency')
        axes[1, 0].set_ylabel('Efficiency Score')
        
        # Plot rank distribution
        axes[1, 1].hist(self.metrics['rank_distribution'], bins=10)
        axes[1, 1].set_title('Rank Distribution')
        axes[1, 1].set_ylabel('Frequency')
        
        plt.tight_layout()
        return fig

# Example usage
analyzer = LoRAAnalyzer()

# Simulate training progression
input_dim, output_dim = 768, 768
ranks = [4, 8, 16, 32]
num_steps = 100

for step in range(num_steps):
    # Simulate training metrics
    rank = np.random.choice(ranks)
    loss = 1.0 * np.exp(-step/50)  # Simulated decreasing loss
    
    analyzer.track_metrics(loss, rank, input_dim, output_dim)

# Generate visualization
fig = analyzer.plot_metrics()

print("Final metrics:")
print(f"Average memory usage: {np.mean(analyzer.metrics['memory_usage']):.2%}")
print(f"Final loss: {analyzer.metrics['training_loss'][-1]:.4f}")
print(f"Average efficiency: {np.mean(analyzer.metrics['adaptation_efficiency']):.4f}")

Hybrid LoRA with Pruning

Implementation of a hybrid approach combining LoRA with weight pruning techniques to further optimize memory usage and computational efficiency during fine-tuning.

import numpy as np
from typing import Dict, Tuple

class HybridLoRAPruning:
    def __init__(self, 
                 input_dim: int,
                 output_dim: int,
                 lora_rank: int = 8,
                 pruning_threshold: float = 0.1):
        self.input_dim = input_dim
        self.output_dim = output_dim
        self.rank = lora_rank
        self.threshold = pruning_threshold
        
        # Initialize LoRA matrices
        self.A = np.random.randn(input_dim, lora_rank) / np.sqrt(lora_rank)
        self.B = np.random.randn(lora_rank, output_dim) / np.sqrt(lora_rank)
        
        # Pruning masks
        self.A_mask = np.ones_like(self.A)
        self.B_mask = np.ones_like(self.B)
        
        # Importance scores
        self.importance_scores = np.zeros((lora_rank,))
        
    def _update_importance_scores(self):
        """Update importance scores based on parameter magnitudes"""
        a_scores = np.sum(np.abs(self.A), axis=0)
        b_scores = np.sum(np.abs(self.B), axis=1)
        self.importance_scores = a_scores * b_scores
        
    def prune_ranks(self):
        """Prune less important ranks based on threshold"""
        self._update_importance_scores()
        mask = self.importance_scores > (np.max(self.importance_scores) * self.threshold)
        
        # Apply masks
        self.A_mask = self.A_mask * mask[np.newaxis, :]
        self.B_mask = self.B_mask * mask[:, np.newaxis]
        
        # Apply pruning
        self.A = self.A * self.A_mask
        self.B = self.B * self.B_mask
        
        return np.sum(mask) / len(mask)  # Return pruning ratio
        
    def forward(self, x: np.ndarray) -> np.ndarray:
        """Forward pass with pruned LoRA matrices"""
        return x @ (self.A @ self.B)
    
    def update(self, 
               gradients: Dict[str, np.ndarray], 
               learning_rate: float = 0.001):
        """Update weights while maintaining pruning"""
        self.A -= learning_rate * gradients['A'] * self.A_mask
        self.B -= learning_rate * gradients['B'] * self.B_mask

# Example usage and evaluation
model = HybridLoRAPruning(input_dim=512, output_dim=512)
batch_size = 16

# Training simulation
for epoch in range(5):
    # Generate sample data
    x = np.random.randn(batch_size, 512)
    target = np.random.randn(batch_size, 512)
    
    # Forward pass
    output = model.forward(x)
    
    # Simulate gradients
    grad_output = output - target
    grad_A = x.T @ (grad_output @ model.B.T)
    grad_B = model.A.T @ (x.T @ grad_output)
    
    # Update weights
    model.update({'A': grad_A, 'B': grad_B})
    
    # Periodic pruning
    if epoch % 2 == 0:
        retention_ratio = model.prune_ranks()
        print(f"Epoch {epoch}, Retained ranks: {retention_ratio:.2%}")
        
        # Calculate sparsity
        sparsity_A = np.mean(model.A_mask == 0)
        sparsity_B = np.mean(model.B_mask == 0)
        print(f"Sparsity - A: {sparsity_A:.2%}, B: {sparsity_B:.2%}")

# Final compression statistics
total_params = model.input_dim * model.output_dim
lora_params = np.sum(model.A_mask) + np.sum(model.B_mask)
compression_ratio = lora_params / total_params
print(f"\nFinal compression ratio: {compression_ratio:.2%}")
print(f"Effective rank: {np.sum(model.importance_scores > 0)}")

Additional Resources

Additional reading recommendations:

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